Debug Render Deployments

Analyze deployment failures using logs, metrics, and database queries. Identify root causes and apply fixes.

When to Use This Skill

Activate this skill when:

• Deployment fails on Render
• Service won't start or keeps crashing
• User mentions errors, logs, or debugging
• Health checks are timing out
• Application errors in production
• Performance issues (slow responses)
• Database connection problems

Render Access

Before reading logs, metrics, or database state, follow references/render-access.md to establish access and verify the intended workspace. Prefer MCP when available; the CLI can provide logs and deploy status, while metrics and structured database queries require MCP.

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Debugging Workflow

When the failure involves configuration or secrets, read references/environment-variables.md before inspecting or changing service variables. When the failure involves deploy state, rollout, health checks, restart, rollback, or shutdown, read references/deployments.md before diagnosing it.

Step 1: Identify Failed Service

list_services()

If MCP isn't configured, ask whether to set it up (preferred) or continue with CLI. Then proceed.

Look for services with failed status. Get details:

get_service(serviceId: "<id>")

Step 2: Retrieve Logs

Build/Deploy Logs (most failures):

list_logs(resource: ["<service-id>"], type: ["build"], limit: 200)

Runtime Error Logs:

list_logs(resource: ["<service-id>"], level: ["error"], limit: 100)

Search for Specific Errors:

list_logs(resource: ["<service-id>"], text: ["KeyError", "ECONNREFUSED"], limit: 50)

HTTP Error Logs:

list_logs(resource: ["<service-id>"], statusCode: ["500", "502", "503"], limit: 50)

Step 3: Analyze Error Patterns

Match log errors against known patterns:

Error	Log Pattern	Common Fix
MISSING_ENV_VAR	KeyError, not defined	Add to render.yaml or update_environment_variables
PORT_BINDING	EADDRINUSE	Use 0.0.0.0:$PORT
MISSING_DEPENDENCY	Cannot find module	Add to package.json/requirements.txt
DATABASE_CONNECTION	ECONNREFUSED :5432	Check DATABASE_URL, DB status
HEALTH_CHECK	Health check timeout	Add /health endpoint, check port binding
OUT_OF_MEMORY	heap out of memory, exit 137	Optimize memory or upgrade plan
BUILD_FAILURE	Command failed	Fix build command or dependencies

Full error catalog: references/error-patterns.md

If errors repeat across deploys: Switch from incremental fixes to a broader sweep. Scan the codebase/config for all likely causes in that error class (related env vars, build config, dependencies, or type errors) and address them together before the next redeploy.

Step 4: Check Metrics (Performance Issues)

For crashes, slow responses, or resource issues:

get_metrics(
  resourceId: "<service-id>",
  metricTypes: ["cpu_usage", "memory_usage", "memory_limit"]
)

get_metrics(
  resourceId: "<service-id>",
  metricTypes: ["http_latency"],
  httpLatencyQuantile: 0.95
)

Detailed metrics guide: references/metrics-debugging.md

Step 5: Debug Database Issues

For database-related errors:

# Check database status
list_postgres_instances()

# Check connections
get_metrics(resourceId: "<postgres-id>", metricTypes: ["active_connections"])

# Query directly
query_render_postgres(
  postgresId: "<postgres-id>",
  sql: "SELECT state, count(*) FROM pg_stat_activity GROUP BY state"
)

Detailed database guide: references/database-debugging.md

Step 6: Apply Fix

For environment variables:

update_environment_variables(
  serviceId: "<service-id>",
  envVars: [{"key": "MISSING_VAR", "value": "value"}]
)

For code changes:

1. Edit the source file
2. Commit and push
3. Deploy triggers automatically (if auto-deploy enabled)

Step 7: Verify Fix

# Check deploy status
list_deploys(serviceId: "<service-id>", limit: 1)

# Check for new errors
list_logs(resource: ["<service-id>"], level: ["error"], limit: 20)

# Check metrics
get_metrics(resourceId: "<service-id>", metricTypes: ["http_request_count"])

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Quick Workflows

Pre-built debugging sequences for common scenarios:

Scenario	Workflow
Deploy failed	list_deploys → list_logs(type: build) → fix → redeploy
App crashing	list_logs(level: error) → get_metrics(memory) → fix
App slow	get_metrics(http_latency) → get_metrics(cpu) → query_postgres
DB connection	list_postgres → get_metrics(connections) → query_postgres
Post-deploy check	list_deploys → list_logs(error) → get_metrics

Detailed workflows: references/quick-workflows.md

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Quick Reference

MCP Tools

# Service Discovery
list_services()
get_service(serviceId: "<id>")
list_postgres_instances()

# Logs
list_logs(resource: ["<id>"], level: ["error"], limit: 100)
list_logs(resource: ["<id>"], type: ["build"], limit: 200)
list_logs(resource: ["<id>"], text: ["search"], limit: 50)

# Metrics
get_metrics(resourceId: "<id>", metricTypes: ["cpu_usage", "memory_usage"])
get_metrics(resourceId: "<id>", metricTypes: ["http_latency"], httpLatencyQuantile: 0.95)

# Database
query_render_postgres(postgresId: "<id>", sql: "SELECT ...")

# Deployments
list_deploys(serviceId: "<id>", limit: 5)

# Environment Variables
update_environment_variables(serviceId: "<id>", envVars: [{key, value}])

CLI Commands (Fallback)

render services -o json
render logs -r <service-id> --level error -o json
render logs -r <service-id> --tail -o text
render deploys create <service-id> --wait

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References

• Render access: references/render-access.md
• Environment variables: references/environment-variables.md
• Compute plans: references/compute-plans.md
• Deployments: references/deployments.md
• Error patterns: references/error-patterns.md
• Metrics debugging: references/metrics-debugging.md
• Database debugging: references/database-debugging.md
• Quick workflows: references/quick-workflows.md
• Log analysis: references/log-analysis.md
• Troubleshooting: references/troubleshooting.md

Related Skills

• render-deploy — Deploy new applications to Render
• render-monitor — Ongoing service health monitoring
• render-mcp — MCP server setup and capability discovery

Current documentation retrieval

Whenever this skill directs you to consult current Render documentation:

1. Retrieve the linked Markdown document directly with an available URL-fetching tool or HTTP client, such as curl. Do not substitute web-search summaries for the document.
2. Confirm that retrieval succeeded and returned the expected document, then read its contents. Saving a file or printing its path is not sufficient.
3. If the request fails or your tool cannot read the Markdown response, open and read the linked HTML version instead.
4. If neither version can be retrieved, disclose that the current reference is unavailable and follow any topic-specific fallback in the skill. Use bundled guidance only for stable constraints, and do not guess at changeable platform details.

When a task requires multiple references, apply this workflow to each one and distinguish the documents you verified from those that remain unavailable.